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td-fft

Performs Fast Fourier Transform analysis on time series data using Teradata's UAF TD_FFT function.

7|Updated Dec 3, 2025
Search Tags:#time series#signal processing#fft#frequency analysis#teradata uaf#spectral decomposition
One-click install
npx skills add https://github.com/teradata-labs/claude-cookbooks --skill td-fft
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: td-fft
Source: https://github.com/teradata-labs/claude-cookbooks/tree/main/skills/analytics/td-fft
Command: npx skills add https://github.com/teradata-labs/claude-cookbooks --skill td-fft

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex process of performing Fast Fourier Transforms (FFT) on time series data, enabling deep analysis of signal frequencies and patterns without requiring expert knowledge of the Teradata Unbounded Array Framework (UAF).

Core Features & Use Cases

  • Frequency Domain Analysis: Decomposes time series signals into their constituent frequencies.
  • Spectral Decomposition: Identifies dominant frequencies, harmonics, and noise components.
  • Use Case: Analyze sensor data from industrial machinery to detect abnormal vibrations at specific frequencies, predicting potential equipment failure before it occurs.

Quick Start

Analyze the time series table 'database.sensor_data' with its timestamp and value columns using the td-fft skill.

Frequently Asked Questions about td-fft

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform frequency domain analysis on time series data in Teradata?▼

Frequency domain analysis in Teradata is performed using the Fast Fourier Transform to decompose time series signals into constituent frequencies. This Skill automates the process via the Teradata Unbounded Array Framework TD_FFT function.

Do I need a specific Teradata license to run FFT analysis on my datasets?▼

Yes, FFT analysis requires Teradata Vantage with the Unbounded Array Framework enabled. You must also have the appropriate licensing for UAF functions to execute the spectral decomposition operations.

What is the best way to identify dominant frequencies in sensor data for predictive maintenance?▼

Spectral decomposition via Fast Fourier Transform is the best way to identify dominant frequencies in sensor data. By transforming the time series into the frequency domain, you can detect abnormal vibrations and predict equipment failure.

Can I use spectral decomposition to detect harmonics and noise in large datasets?▼

Yes, spectral decomposition extracts frequency domain information to isolate harmonics and noise components in large datasets. The Unbounded Array Framework processes the time series signals directly within Teradata Vantage.

Why does time series frequency analysis require the Unbounded Array Framework?▼

Time series frequency analysis requires the Unbounded Array Framework because UAF provides the underlying TD_FFT function. This framework handles the complex mathematical operations needed to transform large datasets into the frequency domain.

What are the limitations of using td-fft for signal processing?▼

The primary limitation is the strict dependency on Teradata Vantage with UAF enabled and properly licensed. Without this specific environment, the Fast Fourier Transform signal processing operations cannot execute.

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